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Record W2344077057 · doi:10.1149/ma2016-01/2/359

On the Key Role of the Carbon Conductive Additive on the Performance of Si-Based Electrodes with High Areal Capacities

2016· article· en· W2344077057 on OpenAlexaff
Zouina Karkar, Driss Mazouzi, Cuauhtémoc Reale Hernandez, Dominique Guyomard, Lionel Roué, Bernard Lestriez

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicSemiconductor materials and devices
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials scienceSiliconAnodeGraphiteElectrodeElectrolyteGravimetric analysisChemical engineeringLithium (medication)NanotechnologyElectrical conductorCarbon fibersCurrent collectorComposite materialMetallurgyChemistryComposite numberOrganic chemistry

Abstract

fetched live from OpenAlex

Silicon-based electrode is a promising candidate in lithium-ion batteries (LIB) due to its significantly higher gravimetric capacity (3579 mAh g-1) in comparison to that of graphite (372 mAh g-1). However, during the process of lithiation/delithiation, the silicon material suffers from a huge volume change which has a negative repercussion on the electrode cycle life through the fracturing of the silicon particles and of the solid electrolyte interphase layer (SEI) and the disconnection of inter-particle contacts. Our group has recently shown that high performance silicon-based anodes can be achieved by combining (i) the use of high-energy ball-milling as a cheap and easy process to produce nanostructured silicon powder, (ii) the processing of the electrode with carboxymethylcellulose (CMC) binder at pH 3 condition, which has been proved to be able to promote the covalent grafting of the CMC to the Si particles; (iii)the use of fluoroethylene and vinylene carbonates (FEC/VC) electrolyte additives resulting in a more stable SEI. (1) One of the biggest challenges of commercializing silicon anodes is to reach a high areal capacity of more than 4 mAh cm-2, in order to achieve a volumetric energy density improvement over the use of conventional graphite-based anodes. Electrodes with such high areal capacity require careful design of their formulation at different scales, and in particular a special attention must be paid to the tailoring of durable intimate contacts between the active material particles and the conductive additive network so that sufficient electron transfer could be achieved throughout the electrode from the copper current collector.(2) Here, silicon-based electrodes of various areal capacities were prepared by using either carbon black (Super P, Timcal), vapor grown carbon nanofibers (VGCFs, Showa Denko), or graphite nanoplatelets (GM15, XGSciences) as conductive additive. These electrodes were examined by using SEM, XRD, Raman, electrical four-probe method and galvanostatic charge/discharge tests. The objective was to establish the relationships between the characteristics of the carbon additive and the electrochemical performance of the electrode. It was observed that the electrical conductivity, capacity retention, and coulombic efficiency of the silicon electrode are significantly affected by the shape, surface area, particle size and crystallinity of the used carbon additives. Spherical-shaped carbon black particles tend to agglomerate and fail in creating a conductive network resilient to the silicon particles’ volume variation. In contrast, vapor grown carbon nanofibers maintain more durable contacts with silicon particles by forming a more resilient conductive network due to their wire-like structure compared to carbon black.(3) Graphite nanoplatelets also create a continuous conductive network and seem to limit the mechanical degradation of the electrode coating, likely by playing the role of electrically conducting lubricant. (4) These results demonstrate that the choice of the conductive additive is of crucial importance for the optimization of silicon negative electrodes with commercially relevant areal capacities. References (1) Gauthier, M.; Mazouzi, D.; Reyter, D.; Lestriez, B.; Moreau, P.; Guyomard, D.; Roué, L. Energy Environ. Sci. 2013, 6(7), 2145. (2) Mazouzi, D.; Karkar, Z.; Reale Hernandez, C.; Jimenez Manero, P.; Guyomard, D.; Roué, L.; Lestriez, B. J. Power Sources 2015, 280, 533–549. (3) Lestriez, B.; Desaever, S.; Danet, J.; Moreau, P.; Plée, D.; Guyomard, D. Electrochem. Solid-State Lett. 2009, 12(4), A76. (4) Nguyen, B. P. N.; Gaubicher, J.; Lestriez, B. Electrochimica Acta 2014, 120, 319–326. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.172
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
Admission routes1
Has abstractyes

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